Tag
#explainer
Every story tagged explainer, newest first.
Quantization Explained: What Q4, Q8, and FP16 Actually Do to a Local Model
Quantization shrinks AI model weights from 16-bit floats to 4- or 8-bit. Here is exactly what that trade-off costs you, and how to pick between Q4_K_M, Q8, and FP16 for the model you actually want to run, with an interactive memory calculator.
BitByteCore Silicon Desk · Aug 6, 2026 · 8 min read
Mixture-of-Experts Models: How They Work and Why They Cut Inference Costs
MoE models activate only a fraction of their parameters per token: DeepSeek-V3 fires 37B of 671B, delivering large-model quality at small-model compute cost. How routers, experts, and load balancing actually work, and where the savings are and are not real. With an interactive dense-vs-MoE view.
BitByteCore Silicon Desk · Aug 6, 2026 · 10 min read
RISC-V vs ARM vs x86: The Honest Comparison
Three instruction set architectures, three very different business models. The RISC-versus-CISC debate is mostly a distraction; what actually decides the industry is licensing, ecosystem, and momentum. A clear-eyed look at x86, ARM, and RISC-V, and how to choose between them.
BitByteCore Silicon Desk · Jul 29, 2026 · 10 min read
Model Distillation: How Small Models Learn to Punch Above Their Weight
Training a tiny model to mimic a giant one sounds like a compromise. It is actually a distinct training discipline, and done right it produces models that beat their parameter count in the ways that matter for shipping AI. With an interactive look at the dark knowledge a soft label carries.
BitByteCore Silicon Desk · Jul 28, 2026 · 9 min read
RAG vs Fine-Tuning: Which One Your Use Case Actually Needs
RAG and fine-tuning solve different problems, knowledge versus behavior, and picking the wrong one wastes money and ships worse results. A practical decision guide with an interactive picker, including why a million-token context window does not retire retrieval.
BitByteCore Silicon Desk · Jul 27, 2026 · 10 min read
